🐔🐥🐓 CluckCare is a website harnessing the power of deep learning convolutional neural networks (CNN-VGG) to predict chicken diseases from uploaded images of their excretions. Our simple yet effective approach aims to assist poultry farmers and veterinarians in promptly identifying potential health issues.

https://cluckcare.onrender.com

The app is hosted on render cloud with the help of a docker image for you to try it out.

If the website is not responding immediately please give it a few minutes since the container instances will scale down to zero after a period of in activity.

• Clone this repo and install the requirements.txt
• Run the app.py file

Convolutional Neural Network - Visual Geometry Group (CNN-VGG) model implemented in TensorFlow/Keras for image classification. The model is trained on a dataset consisting of images belonging to three different classes.

Model Metrics

Accuracy: 96.50%

Precision: 97.00%

Recall: 97.00%

F1 Score: 97.00%

To train the CNN-VGG model for your own dataset, follow these instructions:

1. Ensure Dependencies:

   • Make sure you have Jupyter Notebook installed along with the required Python libraries mentioned in the provided Model Training.ipynb file.

2. Prepare Your Dataset:

   • you can download the Dataset used in this project either from Kaggle https://www.kaggle.com/datasets/ramkishore1/bird-disease-dataset
   • Load the dataset from Kaggle or Replace it with your own dataset.
   • I have dropped the NCD class in my version of the code since it has a very low number of samples.

3. Run the Notebook:

   • Open the Model Training.ipynb notebook in Jupyter Notebook.
   • Execute each cell in the notebook sequentially to load the dataset, preprocess the data, train the model, and save the trained model.

4. Adjust Hyperparameters (Optional):

   • You can adjust the hyperparameters such as callbacks, learning rate, batch size, and number of epochs according to your requirements.

5. Save the Trained Model:

   • After training, the model will be saved as model.h5 in the same directory.
   • You can use this trained model for inference in the web app app.py provided in this repository.

6. Evaluate Model Performance (Optional):

   • Optionally, you can evaluate the performance of your trained model on a separate test dataset to assess its accuracy and other metrics.

7. Customize as Needed:

   • Feel free to customize the notebook or extend the functionality based on your specific requirements.
   • You can also integrate additional features or improve the model architecture for better performance.